Correlation temporal feature extraction network via residual network for English relation extraction

Authors

DOI:

https://doi.org/10.4108/eai.19-11-2021.172213

Keywords:

English relation extraction, correlation temporal feature extraction network, residual network, Softmax

Abstract

In relation extraction, a major challenge is the absence of annotated samples. Relation extraction aims to extract the relationships between entity pairs from a large amount of unstructured data. To solve the above problems, this paper presents a new method for English relation extraction based on correlation temporal feature extraction network via residual network. Firstly, the attention mechanism and recurrent neural network are used to obtain the temporal features of English word correlation. Secondly, a multi-branch feature sensing convolutional neural network is constructed to obtain global and local temporal correlation features respectively. Residual network can dynamically reduce the influence of noise data and better extract the deep information of English text. Finally, the relation extraction is realized with Softmax classifier. Experimental results show that the proposed method can extract English relation effectively than other methods.

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Published

19-11-2021

How to Cite

1.
Li P. Correlation temporal feature extraction network via residual network for English relation extraction. EAI Endorsed Scal Inf Syst [Internet]. 2021 Nov. 19 [cited 2024 Nov. 14];9(36):e3. Available from: https://publications.eai.eu/index.php/sis/article/view/303